def convertDataToEmbeddingTensors(self, dataJSONFile, dataStats):
"""
Reads in JSON file with SNLI sentences and convert to embedding tensor. Note
sentences below maxLength are padded with zeros.
:param dataJSONFile: Path to file with SNLI sentences ('train', 'dev', or 'test')
:param dataStats: Stats about max sent length/vocab size in data file
:return:
"""
sentences = loadExampleSentences(dataJSONFile)
with open(dataStats, "r") as statsFile:
statsJSON = json.load(statsFile)
maxSentLengthPremise = statsJSON["maxSentLenPremise"]
maxSentLengthHypothesis = statsJSON["maxSentLenHypothesis"]
numSent = len(sentences)
premiseTensor = np.zeros((maxSentLengthPremise, numSent, self.dimEmbeddings), dtype=np.float32)
hypothesisTensor = np.zeros((maxSentLengthHypothesis, numSent, self.dimEmbeddings), dtype=np.float32)
for idx, (premiseSent, hypothesisSent) in enumerate(sentences):
premiseIdxMat = self.convertSentListToIdxMatrix(premiseSent)
premiseEmbedList = self.convertIdxMatrixToEmbeddingList(premiseIdxMat)
premiseTensor[0 : len(premiseEmbedList), idx, :] = premiseEmbedList # Pad with zeros at end
hypothesisIdxMat = self.convertSentListToIdxMatrix(hypothesisSent)
hypothesisEmbedList = self.convertIdxMatrixToEmbeddingList(hypothesisIdxMat)
hypothesisTensor[0 : len(hypothesisEmbedList), idx, :] = hypothesisEmbedList
return premiseTensor, hypothesisTensor
def convertDataToIdxMatrices(self, dataJSONFile, dataStats, pad='right'):
"""
Converts data file to matrix of dim (# maxlength, # numSamples, 1)
where the last dimension stores the idx of the word embedding.
:param dataJSONFile: File to data with sentences
:param dataStats:
:param pad: Whether to pad with zeros at beginning (left) or end (right)
:return:
"""
sentences = loadExampleSentences(dataJSONFile)
with open(dataStats, "r") as statsFile:
statsJSON = json.load(statsFile)
maxSentLengthPremise = statsJSON["maxSentLenPremise"]
maxSentLengthHypothesis = statsJSON["maxSentLenHypothesis"]
numSent = len(sentences)
premiseIdxMatrix = np.zeros((maxSentLengthPremise, numSent, 1),
dtype=np.float32)
hypothesisIdxMatrix = np.zeros((maxSentLengthHypothesis, numSent, 1),
dtype=np.float32)
# Fill with 'nan' so that we get random embeddings for words that don't exist
premiseIdxMatrix.fill(np.nan)
hypothesisIdxMatrix.fill(np.nan)
for idx, (premiseSent, hypothesisSent) in enumerate(sentences):
premiseIdxMat = np.array(
self.convertSentListToIdxMatrix(premiseSent))[:, 0]
hypothesisIdxMat = np.array(
self.convertSentListToIdxMatrix(hypothesisSent))[:, 0]
if pad == 'right':
# Pad with zeros at end
premiseIdxMatrix[0:len(premiseIdxMat), idx,
0] = premiseIdxMat # Slight bug here
hypothesisIdxMatrix[0:len(hypothesisIdxMat), idx,
0] = hypothesisIdxMat
else:
# Pad with zeros at beginning
premiseIdxMatrix[-len(premiseIdxMat):, idx,
0] = premiseIdxMat # Slight bug here
hypothesisIdxMatrix[-len(hypothesisIdxMat):, idx,
0] = hypothesisIdxMat
return premiseIdxMatrix, hypothesisIdxMatrix
def convertDataToIdxMatrices(self, dataJSONFile, dataStats, pad="right"):
"""
Converts data file to matrix of dim (# maxlength, # numSamples, 1)
where the last dimension stores the idx of the word embedding.
:param dataJSONFile: File to data with sentences
:param dataStats:
:param pad: Whether to pad with zeros at beginning (left) or end (right)
:return:
"""
sentences = loadExampleSentences(dataJSONFile)
with open(dataStats, "r") as statsFile:
statsJSON = json.load(statsFile)
maxSentLengthPremise = statsJSON["maxSentLenPremise"]
maxSentLengthHypothesis = statsJSON["maxSentLenHypothesis"]
numSent = len(sentences)
premiseIdxMatrix = np.zeros((maxSentLengthPremise, numSent, 1), dtype=np.float32)
hypothesisIdxMatrix = np.zeros((maxSentLengthHypothesis, numSent, 1), dtype=np.float32)
# Fill with 'nan' so that we get random embeddings for words that don't exist
premiseIdxMatrix.fill(np.nan)
hypothesisIdxMatrix.fill(np.nan)
for idx, (premiseSent, hypothesisSent) in enumerate(sentences):
premiseIdxMat = np.array(self.convertSentListToIdxMatrix(premiseSent))[:, 0]
hypothesisIdxMat = np.array(self.convertSentListToIdxMatrix(hypothesisSent))[:, 0]
if pad == "right":
# Pad with zeros at end
premiseIdxMatrix[0 : len(premiseIdxMat), idx, 0] = premiseIdxMat # Slight bug here
hypothesisIdxMatrix[0 : len(hypothesisIdxMat), idx, 0] = hypothesisIdxMat
else:
# Pad with zeros at beginning
premiseIdxMatrix[-len(premiseIdxMat) :, idx, 0] = premiseIdxMat # Slight bug here
hypothesisIdxMatrix[-len(hypothesisIdxMat) :, idx, 0] = hypothesisIdxMat
return premiseIdxMatrix, hypothesisIdxMatrix
def convertDataToEmbeddingTensors(self, dataJSONFile, dataStats):
"""
Reads in JSON file with SNLI sentences and convert to embedding tensor. Note
sentences below maxLength are padded with zeros.
:param dataJSONFile: Path to file with SNLI sentences ('train', 'dev', or 'test')
:param dataStats: Stats about max sent length/vocab size in data file
:return:
"""
sentences = loadExampleSentences(dataJSONFile)
with open(dataStats, "r") as statsFile:
statsJSON = json.load(statsFile)
maxSentLengthPremise = statsJSON["maxSentLenPremise"]
maxSentLengthHypothesis = statsJSON["maxSentLenHypothesis"]
numSent = len(sentences)
premiseTensor = np.zeros(
(maxSentLengthPremise, numSent, self.dimEmbeddings),
dtype=np.float32)
hypothesisTensor = np.zeros(
(maxSentLengthHypothesis, numSent, self.dimEmbeddings),
dtype=np.float32)
for idx, (premiseSent, hypothesisSent) in enumerate(sentences):
premiseIdxMat = self.convertSentListToIdxMatrix(premiseSent)
premiseEmbedList = self.convertIdxMatrixToEmbeddingList(
premiseIdxMat)
premiseTensor[0:len(premiseEmbedList),
idx, :] = premiseEmbedList # Pad with zeros at end
hypothesisIdxMat = self.convertSentListToIdxMatrix(hypothesisSent)
hypothesisEmbedList = self.convertIdxMatrixToEmbeddingList(
hypothesisIdxMat)
hypothesisTensor[0:len(hypothesisEmbedList),
idx, :] = hypothesisEmbedList
return premiseTensor, hypothesisTensor
